| Data periods | Mode of travel | Approximate device count | Approximate trip count |
|---|---|---|---|
| Jan 01, 2021 - Dec 31, 2021 | All Vehicles LBS Plus - StL All Vehicles Volume | 1,038,000 | 125,411,000 |
8 Data sources
8.1 StreetLight Data
StreetLight Data is a transportation analytics platform that uses aggregated location-based services (LBS) data from cell phones and navigation/GPS data to deliver insights on travel patterns. For this project, we used StreetLight to find the volume of traffic (number of vehicles) and average trip length for passenger and commercial vehicles.
For ease of access, we used {streetlightR} to interact directly with the StreetLight API. {streetlightR} is an open-source R package maintained by Council staff.
Data source description, type
- StreetLight Data, falls in second rank. Quality rank (See Table B.2)
- How, when, and why was the data collected?
- If this is a modeled dataset, what is the sample?
- What is the raw unit of measurement?
- How was this data accessed? Include any relevant links/citations, code, or downloads.
- What data cleaning or wrangling was completed? How did you test these processes and outputs?
- What is the geographic and temporal scope? Did you complete any aggregation?
- What version is the data? Were there other versions available? If so, why did you choose this version?
- What assumptions are made when we use this dataset?
- Which subject matter expert (SME) reviewed this data?
- Describe testing used to verify data
Be sure to add a citation of this dataset to the Zotero shared library.
8.1.1 Data characteristics
- Were there any missing data? How did you handle missing data?
- Plots, tables, and description of data distribution
- Variance, Z-Score, quantiles
- Facet views by categorical variables
8.1.2 Passenger
For passenger data, we used StreetLight Volume - an estimate of the number of vehicles. The models that make up StreetLight Volume predict vehicle volumes by combining Location-Based Services (LBS) trips with contextual features that represent the street network, demographics, climate, and other geographic characteristics (StreetLight Data 2023a). The models are validated against data permanent traffic counters across the country, including in the study area.
8.1.2.1 Trip length validation
StreetLight returns not only vehicle volume, but also trip attributes, like trip length. We then use this to estimate vehicle miles traveled, by multiplying volume by average trip length for each origin-destination pair.
To test logical validity of average trip lengths, we will compare the minimum distance between each origin and destination with the average trip length. These should correlate.
In cases where the origin and destination counties are not adjacent, the average trip length is consistently higher than the minimum distance between the counties.
We can also compare these distances with the observed average trip distance from the Met Council Travel Behavior Inventory (TBI). Read more about the TBI in Section 8.5.
We would also expect that large counties will have longer trip lengths and smaller counties will have shorter trip lengths.
Comparing trip distance and county area, we see a general positive correlation (the larger the county, the longer the average trip).
8.1.3 Commercial
StreetLight does not provide StreetLight Volume for 2021 commercial vehicle analyses. To measure volume for commercial traffic, we used the StreetLight Index, a relative measure of traffic volume, calibrated using AADT values to result in traffic volume (StreetLight Data 2023b).
StreetLight compares the AADT calibration values for a given zone with StreetLight’s sample size for the same zone, and creates a calibration factor to apply to the entire analysis (StreetLight Data 2023d). We generated a calibration zone set for commercial traffic by selecting road segments with both AADT and vehicle classification data in both MN and WI counties within the CPRG study area. Read more about state DOT vehicle weight distribution data in Section 8.3.0.2.
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| Data periods | Mode of travel | Vehicle weight | Approximate device count | Approximate trip count |
|---|---|---|---|---|
| Jan 01, 2021 - Dec 31, 2021 | Truck - StL Calibrated Truck Index | Medium | N/A | 1,514,000 |
| Jan 01, 2021 - Dec 31, 2021 | Truck - StL Calibrated Truck Index | Heavy | N/A | 605,000 |
Then, we selected only the stations within the study area with observations in the last five years (2017-2021). Finally, we joined this data with Average Annual Daily Traffic (AADT) WisDOT (2021) road segments by station ID. The road segments sampled include multiple road functional classes and segments in all seven metro counties. All traffic sensor stations pulled were permanent, continuous monitoring sites.
8.1.3.1 Calibration
StreetLight classifies commercial vehicles by Federal Highway Administration (FHWA) gross vehicle weight range (GWVR) classes: where vehicles between 14,000 lbs and 26,000 lbs (Class 4 to Class 6) are considered medium-duty, and vehicles greater than 26,000 lbs (Class 7+) are heavy-duty (StreetLight Data 2023c).
EPA’s Motor Vehicle Emissions Simulator (MOVES4) has their own, slightly different vehicle classification system (USEPA 2023b).
After reviewing MnDOT’s visual definitions of commercial vehicles, we defined MnDOT vehicle types 4-7 as medium-duty and types 8-13 as heavy-duty. We believe this configuration aligns most closely with both StreetLight’s and MOVES4’s vehicle classifications schemes.
However, vehicles falling in FHWA class 7 (> 26,000 lbs, < 33,000 lbs) are classified as medium duty by state DOTs, and heavy duty by StreetLight. This discrepancy is relatively small, and is unlikely to heavily influence emisssions reported.
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To create the calibration dataset, we found the ratio of passenger/medium/heavy-duty vehicles for at traffic count stations within our study area using state DOT data. You can read more about vehicle classification data in Section 8.3.0.2.
8.2 Total vehicle volume validation
To validate our county origin-destination VMT data, we can compare the county totals to the DOT reported values from MnDOT (MnDOT 2021a) and WisDOT (WisDOT 2021). Note that these data include all vehicle types, both passenger and commercial.
8.2.1 Limitations
- The data used for passenger traffic contains “all vehicles”, not just passenger vehicles, meaning that commercial vehicles may be double counted. As a default, StreetLight suggests that users use a ratio of 96/2/2 (95% passenger, 2% medium, 2% heavy). We could apply a scaling factor of 0.96 to the passenger data to account for this.
- Commercial vehicle classifications schemes differ across data sources.
8.2.2 Comparison with similar datasets
8.3 State DOT data
As required by federal law, Minnesota and Wisconsin state departments of transportation (MnDOT and WisDOT) report various traffic measures for planning, forecasting, and various analysis endeavors.
8.3.0.1 Vehicle miles travled
Vehicle miles traveled (VMT) is a standardized measure created by multiplying average annual daily traffic (AADT) by centerline miles. AADT is an estimate of the total vehicles on a road segment on any given day of the year in all directions of travel. VMT and AADT are common traffic measures and standardized across the United States.
MnDOT and WisDOT derive VMT using traffic counts from continuous and short term traffic monitoring sites. These raw counts are adjusted by multiplying seasonal, day-of-week, and axle adjustment factors WisDOT (2023). Data is not collected for every site every year, but the data are sufficient for year-over-year comparisons.
These data were compiled from MnDOT and WisDOT county level reports. MnDOT provides Excel workbooks with VMT by county and route system on their website. These were downloaded, filtered to include on the relevant counties, and aggregated to the county level by summing VMT by county/route system. Processing code can be found in mndot_vmt_county.R.
WisDOT publishes PDF tables with county-level VMT. These were downloaded and data extracted using {tabulizer}, an R package interfacing with the Tabula PDF extractor library. Processing code can be found in wisdot_vmt_county.R.
Data source description, type
- Quality rank (See Table B.2)
- How, when, and why was the data collected?
- If this is a modeled dataset, what is the sample?
- What is the raw unit of measurement?
- How was this data accessed? Include any relevant links/citations, code, or downloads.
- What data cleaning or wrangling was completed? How did you test these processes and outputs?
- What is the geographic and temporal scope? Did you complete any aggregation?
- What version is the data? Were there other versions available? If so, why did you choose this version?
- What assumptions are made when we use this dataset?
- Which subject matter expert (SME) reviewed this data?
- Describe testing used to verify data
Be sure to add a citation of this dataset to the Zotero shared library.
8.3.0.1.1 Data characteristics
- Were there any missing data? How did you handle missing data?
- Plots, tables, and description of data distribution
- Variance, Z-Score, quantiles
- Facet views by categorical variables
8.3.0.1.2 Limitations
- Usually only samples county and state roads, primary arterials
- Not every site is sampled every year
8.3.0.2 Vehicle distribution by weight
To calibrate the generalized StreetLight Index to get commercial vehicle counts, we created a set of spatial lines (roads) to calibrate StreetLight’s metrics. For each calibration road, we found the proportion of passenger, medium-, and heavy-duty vehicles in the most recent available year, up to 2021.
State DOTs operate vehicle classification stations, which provide both the volume of traffic on a given road segment and, for some locations, the breakdown of volume by vehicle type. We obtained this breakdown using data from MnDOT (MnDOT 2021b) and WisDOT (WisDOT 2020) reporting websites.
MnDOT provides AADT road segments, which align with station identification numbers. Wisconsin does not readily supply AADT road segment data - as suggested by the Wisconsin cartographers office (State Cartographer’s Office 2021), we pulled OpenStreetMaps road data (OSM version 0.6).
Then, we selected only the stations within the study area with observations in the last five years (2017-2021). Finally, we joined this data with Average Annual Daily Traffic (AADT) WisDOT (2021) road segments by station ID. The road segments sampled include multiple road functional classes and segments in all counties. All traffic sensor stations pulled were permanent, continuous monitoring sites.
Introduction text Data source description, type
- Quality rank (See Table B.2)
- How, when, and why was the data collected?
- If this is a modeled dataset, what is the sample?
- What is the raw unit of measurement?
- How was this data accessed? Include any relevant links/citations, code, or downloads.
- What data cleaning or wrangling was completed? How did you test these processes and outputs?
- What is the geographic and temporal scope? Did you complete any aggregation?
- What version is the data? Were there other versions available? If so, why did you choose this version?
- What assumptions are made when we use this dataset?
- Which subject matter expert (SME) reviewed this data?
- Describe testing used to verify data
Be sure to add a citation of this dataset to the Zotero shared library.
8.3.0.2.1 Data characteristics
- Were there any missing data? How did you handle missing data?
- Plots, tables, and description of data distribution
- Variance, Z-Score, quantiles
- Facet views by categorical variables
8.3.0.2.2 Limitations
8.3.1 Data dictionaries
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Table with detailed description of columns and definitions for each data table.
8.4 EPA MOVES
Emissions rates for our region were calculated using the EPA’s Motor Vehicle Emissions Simulator (MOVES4) (USEPA 2023a). MOVES4 calculates emissions factors using Council’s regional travel demand model, Minnesota Department of Vehicle Services county vehicle registration data, and the Minnesota Pollution Control Agency vehicle age distribution. Each of these inputs helps the model estimate the characteristics of vehicles on the road in our region. The model takes into account differences in fuel economy (miles per gallon) depending on a vehicle’s age and size, as well as its fuel intake (diesel or gasoline). The results are specific to the conditions of our region, and so are more accurate than national averages.
CO2 equivalence (CO2e) values are derived from global warming potential (GWP) values. See Section A.2 for more details.
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Data source description, type
- Quality rank (See Table B.2)
- How, when, and why was the data collected?
- If this is a modeled dataset, what is the sample?
- What is the raw unit of measurement?
- How was this data accessed? Include any relevant links/citations, code, or downloads.
- What data cleaning or wrangling was completed? How did you test these processes and outputs?
- What is the geographic and temporal scope? Did you complete any aggregation?
- What version is the data? Were there other versions available? If so, why did you choose this version?
- What assumptions are made when we use this dataset?
- Which subject matter expert (SME) reviewed this data?
- Describe testing used to verify data
Be sure to add a citation of this dataset to the Zotero shared library.
8.4.1 Data characteristics
- Were there any missing data? How did you handle missing data?
- Plots, tables, and description of data distribution
- Variance, Z-Score, quantiles
- Facet views by categorical variables
8.4.2 Limitations
8.4.3 Comparison with similar datasets
8.4.4 Data dictionary
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>>>>>>> origin/main
Table with detailed description of columns and definitions for each data table.
8.5 Travel Behavior Inventory
The Metropolitan Council Travel Behavior Inventory (TBI) is a bi-annual household survey around 7,500 families in the 7-county Twin Cities metro and three neighboring Wisconsin counties. Information on people, households, trips, and vehicles are collected.
8.5.1 Regional fleet characteristics
We used 2021 TBI data to determine the regional average vehicle age and distribution of diesel and gasoline passenger vehicles.
Vehicles were classified into broad fuel categories - diesel and gas + all other fuels (including gasoline, electric, flex-fuel, hybrid, and plug-in hybrid) to best match the average miles per gallon table specifications in the EPA Local Greenhouse Gas Inventory Tool. The resulting value is on par with recent statistics from the Bureau of Transportation Statistics (BTS), which calculates the average passenger vehicle age in 2021 to be 12.1 years (BTS 2023).
TBI data were cleaned to only include vehicles with complete data and model year 1980 or later.
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8.5.2 Average trip distance between counties
The average trip distance for the entire region is 6.27 miles (standard error 0.14), based on a sample of 117,568 trips.
Trips with distance over 720 miles (the equivalent of 12 hours of driving at 60 miles per hour) were removed.
Introduction text Data source description, type
- Quality rank (See Table B.2)
- How, when, and why was the data collected?
- If this is a modeled dataset, what is the sample?
- What is the raw unit of measurement?
- How was this data accessed? Include any relevant links/citations, code, or downloads.
- What data cleaning or wrangling was completed? How did you test these processes and outputs?
- What is the geographic and temporal scope? Did you complete any aggregation?
- What version is the data? Were there other versions available? If so, why did you choose this version?
- What assumptions are made when we use this dataset?
- Which subject matter expert (SME) reviewed this data?
- Describe testing used to verify data
Be sure to add a citation of this dataset to the Zotero shared library.
8.5.3 Data characteristics
- Were there any missing data? How did you handle missing data?
- Plots, tables, and description of data distribution
- Variance, Z-Score, quantiles
- Facet views by categorical variables
8.5.4 Limitations
8.5.5 Comparison with similar datasets
8.5.6 Data dictionary
Table with detailed description of columns and definitions for each data table.